Field
Aspects of the present invention generally relate to an information processing apparatus suitably used especially for updating a face dictionary used for face authentication, an information processing method, and a storage medium.
Description of the Related Art
Recently, a face detection technology for detecting a face portion from an image and a face authentication technology for specifying a person have been put into practical use. An application used in a personal computer (PC) performs face detection and face authentication on images stored in the PC, and information about the name of a person is added to an image. Thus, the image can be searched for by the name.
For the face authentication, a dictionary referred to as a face dictionary is used. With the face dictionary, the name of the person and face feature amount data for determining the person are registered. In the face authentication, a face is detected from the image to obtain face feature amount data, and similarity between the face feature amount data and the face feature amount data registered with the face dictionary is calculated. Then, when the similarity is equal to or higher than a predetermined value, the detected face is determined to be that of the person registered with the face dictionary. The face dictionary is created by selecting some of images to which the same name of a person has been added. When images to which the same name of a person has been added increase, some are further selected from these images to update the face dictionary.
FIG. 3 illustrates an example of data registered with a conventional face dictionary. The example illustrated in FIG. 3 is the face dictionary of a person A named “YAMADA TARO”. The dictionary includes up to five pieces of face image data and feature amount data extracted from the face image data.
However, it takes long to update such a face dictionary. Thus, for example, Japanese Patent Application Laid-Open No. 2002-269563 discuses a technology for updating the face dictionary at specific timing. According to the technology discussed in Japanese Patent Application Laid-Open No. 2002-269563, the face dictionary is updated in accordance with a predetermined rule based on information about the date of image capturing. For example, updating is performed in accordance with a rule of preferentially updating the face dictionary from a latest image.
When an image of a certain person is searched for, if the similarity of data of persons included in previously managed images is registered with a database (DB), only images having similarity equal to or higher than a predetermined value need to be extracted. Thus, searching to be performed next time can be faster.
FIG. 6 illustrates an example of a face similarity DB created for the managed images. For example, a face feature amount in the image is compared with a feature amount registered with the face dictionary to obtain correlation (similarity), and a result is registered with the face similarity DB.
In FIG. 6, horizontally-aligned columns 601 indicate a list of persons registered with the face dictionary, and vertically-aligned rows 602 indicate a list of faces in the managed images. For example, similarity 603 in feature amount between a person A registered with the face dictionary and a “face 000001” is “51”. Thus, feature amounts of all the persons registered with the face dictionary are compared with feature amounts of all the faces in each image to calculate similarity. When an instruction of searching for a face of a person identical to the person A is received, a face having similarity equal to or higher than a predetermined value, for example, a face of having similarity of 200 or higher, is estimated as the person A, and extracted as a searching result.
As described above, long processing time is necessary for updating the face dictionary. When the face dictionary is updated, the similarity registered with the face similarity DB illustrated in FIG. 6 also changes. Consequently, since the data stored in the face similarity DB needs to be discarded to calculate new similarity, and its result needs to be registered again in the face similarity DB, much longer time is expended.
In the case of the example illustrated in FIG. 6, when the face dictionary of the person A is changed, similarity needs to be calculated again from the “face 000001” to a “face 100000”. Such frequent updating of the face dictionary is not so favorable because a searching speed decreases.
According to the method described in Japanese Patent Application Laid-Open No. 2002-269563, based on image capturing date information, the face dictionary is updated following addition of a new image. Thus, each time an image captured by a camera is loaded into the PC, the face dictionary is updated. Consequently, in a general operation flow where the image captured by the camera is loaded into the PC and is displayed, the face dictionary is updated for each loading.